/* 
 * This file is part of the UEA Time Series Machine Learning (TSML) toolbox.
 *
 * The UEA TSML toolbox is free software: you can redistribute it and/or 
 * modify it under the terms of the GNU General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or 
 * (at your option) any later version.
 *
 * The UEA TSML toolbox is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License along
 * with the UEA TSML toolbox. If not, see <https://www.gnu.org/licenses/>.
 */
 
package tsml.classifiers.shapelet_based;
import experiments.data.DatasetLoading;
import java.io.File;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Random;
import java.util.concurrent.TimeUnit;

import tsml.classifiers.EnhancedAbstractClassifier;
import tsml.classifiers.ParameterSplittable;

import utilities.StatisticalUtilities;
import utilities.InstanceTools;
import static utilities.InstanceTools.fromWekaInstancesArray;
import static utilities.StatisticalUtilities.calculateSigmoid;
import weka.clusterers.SimpleKMeans;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;

/**
 * 
 * @author Original algorithm and adjustments Josef Grabocka, initial conversion
 * Aaron Bostrom 

 * 
 */

public class LearnShapelets extends EnhancedAbstractClassifier implements ParameterSplittable,TechnicalInformationHandler{

  
    @Override
    public TechnicalInformation getTechnicalInformation() {
        TechnicalInformation 	result;
        result = new TechnicalInformation(TechnicalInformation.Type.ARTICLE);
        result.setValue(TechnicalInformation.Field.AUTHOR, "J. Grabocka, N. Schilling, M. Wistuba and L. Schmidt-Thieme");
        result.setValue(TechnicalInformation.Field.TITLE, "Learning Time-Series Shapelets");
        result.setValue(TechnicalInformation.Field.JOURNAL, "Proc. 20th SIGKDD");
        result.setValue(TechnicalInformation.Field.YEAR, "2014");
        return result;
    }    
            
    // length of a time-series 
    public int seriesLength;
    // length of shapelet
    public int[] L;
    // number of latent patterns
    public int K;
    // number of classes
    public int C;
    // number of segments
    public int numberOfSegments[];
    
    int L_min;
    // shapelets
    double[][][] shapelets;
    // classification weights
    double W[][][];
    double biasW[];
    
    // accumulate the gradients
    double[][][] gradHistShapelets;
    double[][][] gradHistW;
    double[] gradHistBiasW;

    // the regularization parameters
    public double lambdaW=0.01;
    // scales of the shapelet length
    public int R=3;
    public double percentageOfSeriesLength=0.2;
    // the learning rate
    public double eta=0.1;
    // the softmax parameter
    public double alpha=-30;
    // the number of iterations
    public int maxIter=300;
    
    public Instances trainSet;
//    public Instance testSet;
    // time series data and the label 
    public double[][] train, classValues_train;
//    public double[] test;



    public List<Double> nominalLabels;

    // structures for storing the precomputed terms
    double D_train[][][][]; //mean square error for each shapelet compared with each shapelet centroid. Formula 20
    double E_train[][][][]; // e^alpha*D_r,i,k,j part of Formula 23.
    double M_train[][][];   //Generalised Soft Minimum. Formula 19.
    double Psi_train[][][]; //Sum 1->j(e^alpha*D_r,i,k,j'). Denominator of Formula 23.
    double sigY_train[][];
    
    double D_test[][][];
    double E_test[][][];
    double M_test[][];
    double Psi_test[][];
    double sigY_test[];
    
    // temporary variables useful for the derivatives of the shapelets
    double [][] tmp2;
    double regWConst, tmp1, tmp3, dLdY, gradW_crk, gradS_rkl, gradBiasW_c, eps = 0.000000000000000000001;

    Random rand = new Random();
    
    // store the indices of the positive and negative instances per each class
    List< List<Integer>> posIdxs;
    List< List<Integer>>  negIdxs;
    
    
    List<Integer> instanceIdxs;
    
    public boolean enableParallel=true;
    
//Parameter search settings
    boolean paraSearch=false;
    
    double[] lambdaWRange = {0.01, 0.1}; 
    double[] percentageOfSeriesLengthRange = {0.15};
    int[] shapeletLengthScaleRange = {2, 4}; 
    public void setParamSearch(boolean b) 
    { 
        paraSearch=b;
        
        //default the values to something.
//AARON has broken this        
//        if(paraSearch)
//           fixParameters();
    }
//Set to defaults recommended by the author    
    public void fixParameters(){
   // the regularization parameters
        lambdaW=0.01;
    // scales of the shapelet length
        R=3;
        percentageOfSeriesLength=0.2;
    // the learning rate
        eta=0.1;
    // the softmax parameter
        alpha=-30;
    // the number of iterations
        maxIter=300;        
    }
/* The actual parameter values should be set internally. This integer
  is just a key to maintain different parameter sets 
    */
    public void setParametersFromIndex(int x){
//Map integer: filthy hack,could be done better. Range is 1-8
        if(x<=4)
            lambdaW=lambdaWRange[0];
        else
            lambdaW=lambdaWRange[1];
        if(x==1 || x==2||x==5||x==6)    
            percentageOfSeriesLength=percentageOfSeriesLengthRange[0];
        else
            percentageOfSeriesLength=percentageOfSeriesLengthRange[1];
        if(x%2==1)
            R=shapeletLengthScaleRange[0];
        else
            R=shapeletLengthScaleRange[1];
    }
    public String getParas(){
        return lambdaW+","+percentageOfSeriesLength+","+R;
    }
    public double getAcc(){
        return maxAcc;
    }
    double maxAcc;
    
    // constructor
    public LearnShapelets() {
        super(CANNOT_ESTIMATE_OWN_PERFORMANCE);
    }
    
    @Override
    public void setSeed(int seed)
    {
        super.setSeed(seed);
        rand = new Random(seed);
    }

    // initialize the data structures
    public void initialize() throws Exception {
        
        // avoid K=0 
        if (K == 0) {
            K = 1;
        }
        
        L_min = (int)(percentageOfSeriesLength * seriesLength);

        // set the labels to be binary 0 and 1, needed for the logistic loss
        createOneVsAllTargets();

        // initialize the shapelets (complete initialization during the clustering)
        shapelets = new double[R][][];
        // initialize the number of shapelets (by their starting point) and the length of the shapelets 
        numberOfSegments = new int[R];
        L = new int[R];
        // set the lengths of shapelets and the number of segments
        // at each scale r
        int totalSegments = 0;
        
        //for each scale we create a number of segments and a shapelet length based on the scale value and our minimum shapelet length.
        for (int r = 0; r < R; r++) {
            L[r] = (r + 1) * L_min;
            numberOfSegments[r] = seriesLength - L[r];

            totalSegments += train.length * numberOfSegments[r];
        }

        // set the total number of shapelets per scale as a rule of thumb
        // to the logarithm of the total segments
        K = (int)(Math.log(totalSegments)*(C-1)); 

        // initialize the terms for pre-computation
        D_train = new double[train.length][R][K][];
        E_train = new double[train.length][R][K][];

        for (int i = 0; i < train.length; i++) {
            for (int r = 0; r < R; r++) {
                for (int k = 0; k < K; k++) {
                    D_train[i][r][k] = new double[numberOfSegments[r]];
                    E_train[i][r][k] = new double[numberOfSegments[r]];
                }
            }
        }
        
        // initialize the placeholders for the precomputed values
        M_train = new double[train.length][R][K];
        Psi_train = new double[train.length][R][K];
        sigY_train = new double[train.length][C];

        // initialize the weights
        W = new double[C][R][K];
        biasW = new double[C];
        
        for (int c = 0; c < C; c++) {
            for (int r = 0; r < R; r++) {
                for (int k = 0; k < K; k++) {
                    W[c][r][k] = 2*eps*rand.nextDouble() - 1;
                }
            }

            biasW[c] = 2*eps*rand.nextDouble() - 1; 
        }
        
        // initialize gradient accumulators
        
        gradHistW = new double[C][R][K];
        gradHistBiasW = new double[C];
        
        gradHistShapelets = new double[R][][];                 
        for(int r=0; r<R; r++) 
             gradHistShapelets[r] = new double[K][ L[r] ]; 
        
        
        initializeShapeletsKMeans(); 
        
        print("Initialization completed: L_min=" + L_min + ", K="+K
                +", R="+R + ", C="+C + ", lambdaW="+lambdaW); 
        
        tmp2 = new double[R][];
        for(int r=0; r<R; r++)
            tmp2[r] = new double[numberOfSegments[r]];
        
        // initialize constant term for the regularization
        regWConst = ((double) 2.0 * lambdaW) / ((double) train.length); 

        // initialize an array of the sizes
        instanceIdxs = new ArrayList<>(); 
        for (int i = 0; i < train.length; i++) {
            instanceIdxs.add(i); 
        }        
    }

    // create one-cs-all targets
    public void createOneVsAllTargets() {
        
        C = nominalLabels.size();

        classValues_train = new double[train.length][C];

        // initialize the extended representation  
        for (int i = 0; i < train.length; i++) {
            // firts set everything to zero
            for (int c = 0; c < C; c++) {
                classValues_train[i][c] =  0;
            }

            // then set the real label index to 1
            int indexLabel = nominalLabels.indexOf(trainSet.get(i).classValue());
            classValues_train[i][indexLabel] = 1.0;
        }
        
        // initialize the index lists
        posIdxs = new ArrayList<List<Integer>>();
        negIdxs = new ArrayList<List<Integer>>(); 
        // store the indices of the positive and negative instances per each class
        for (int c = 0; c < C; c++) {
            
            List<Integer> posIdx_c = new ArrayList<Integer>();
            List<Integer> negIdx_c = new ArrayList<Integer>();
            
            for (int i = 0; i < train.length; i++)
                if( classValues_train[i][c] == 1.0 )
                    posIdx_c.add(i);
                else
                    negIdx_c.add(i);  
            
            posIdxs.add(posIdx_c);
            negIdxs.add(negIdx_c);  
        }
    }
    // initialize the shapelets from a file provided
    public void initializeShapeletsFromFile() throws Exception {
        //for each scale r, i.e. for each set of K shapelets at
        // length L_min*(r+1)
        
        for (int r=0; r<R; r++) {
            double[][] segments_r = new double[train.length * numberOfSegments[r]][L[r]];
            
            //construct the segments from the train set.
            for (int i = 0; i < train.length; i++)
                for (int j = 0; j < numberOfSegments[r]; j++)
                    for (int l = 0; l < L[r]; l++)
                        segments_r[i * numberOfSegments[r] + j][l] = train[i][j + l]; 
                    

            // normalize segments
            for (int i = 0; i < train.length; i++)
                for (int j = 0; j < numberOfSegments[r]; j++)
                    segments_r[i * numberOfSegments[r] + j] = StatisticalUtilities.normalize(segments_r[i * numberOfSegments[r] + j]);

            Instances ins = InstanceTools.toWekaInstances(segments_r); 
            
            SimpleKMeans skm = new SimpleKMeans();
            skm.setNumClusters(K);
            skm.setMaxIterations(100); 
            //skm.setInitializeUsingKMeansPlusPlusMethod(true); 
            skm.setSeed((int) (rand.nextDouble() * 1000) );
            skm.buildClusterer( ins );
            Instances centroidsWeka = skm.getClusterCentroids();
            shapelets[r] =  InstanceTools.fromWekaInstancesArray(centroidsWeka, false);
              
            // initialize the gradient history of shapelets
            if (shapelets[r] == null)
                print("P not set"); 
        }
    }


    
    
    // initialize the shapelets from the centroids of the segments
    public void initializeShapeletsKMeans() throws Exception {
        //for each scale r, i.e. for each set of K shapelets at
        // length L_min*(r+1)
        
        for (int r=0; r<R; r++) {
            double[][] segments_r = new double[train.length * numberOfSegments[r]][L[r]];
            
            //construct the segments from the train set.
            for (int i = 0; i < train.length; i++)
                for (int j = 0; j < numberOfSegments[r]; j++)
                    for (int l = 0; l < L[r]; l++)
                        segments_r[i * numberOfSegments[r] + j][l] = train[i][j + l]; 
                    

            // normalize segments
            for (int i = 0; i < train.length; i++)
                for (int j = 0; j < numberOfSegments[r]; j++)
                    segments_r[i * numberOfSegments[r] + j] = StatisticalUtilities.normalize(segments_r[i * numberOfSegments[r] + j]);

            Instances ins = InstanceTools.toWekaInstances(segments_r); 
            
            SimpleKMeans skm = new SimpleKMeans();
            skm.setNumClusters(K);
            skm.setMaxIterations(100); 
            //skm.setInitializeUsingKMeansPlusPlusMethod(true); 
            skm.setSeed((int) (rand.nextDouble() * 1000) );
            skm.buildClusterer( ins );
            Instances centroidsWeka = skm.getClusterCentroids();
            shapelets[r] =  InstanceTools.fromWekaInstancesArray(centroidsWeka, false);
              
            // initialize the gradient history of shapelets
            if (shapelets[r] == null)
                print("P not set"); 
        }
    }

    // predict the label value vartheta_i
    public double predict_i(double[][] M, int c) 
    {
        
        double Y_hat_ic = biasW[c];

        for (int r = 0; r < R; r++) {
            for (int k = 0; k < K; k++) {
                Y_hat_ic += M[r][k] * W[c][r][k];
            }
        }

        return Y_hat_ic;
    }

    // precompute terms
    public void preCompute(double[][][] D, double[][][] E, double[][] Psi, double[][] M, double[] sigY, double[] series) {
        
        // precompute terms     
        for (int r = 0; r < R; r++) {
            //in most cases Shapelets[r].length == numLatentPatterns, this is not always true.
            for (int k = 0; k < shapelets[r].length; k++) { 
                for(int j = 0; j < numberOfSegments[r]; j++)
                {
                    // precompute D
                    D[r][k][j] = 0;
                    double err = 0;

                    for(int l = 0; l < L[r]; l++)
                    {
                        err = series[j + l] - shapelets[r][k][l];
                        D[r][k][j] += err*err; 
                    }

                    D[r][k][j] /= (double)L[r]; 

                    // precompute E
                    E[r][k][j] = Math.exp(alpha * D[r][k][j]);
                }

                // precompute Psi 
                Psi[r][k] = 0; 
                for(int j = 0; j < numberOfSegments[r]; j++) 
                        Psi[r][k] +=  Math.exp( alpha * D[r][k][j] );

                // precompute M 
                M[r][k] = 0;

                for(int j = 0; j < numberOfSegments[r]; j++)
                        M[r][k] += D[r][k][j]* E[r][k][j];

                M[r][k] /= Psi[r][k];
            }
        }

        for (int c = 0; c < C; c++) {
            sigY[c] = calculateSigmoid(predict_i(M, c));
        }
    }

    // compute the accuracy loss of instance i according to the 
    // logistic loss
    public double accuracyLoss(double[][] M, double[] classValues, int c) {
        double Y_hat_ic = predict_i(M, c);
        double sig_y_ic = calculateSigmoid(Y_hat_ic);

        double returnVal = -classValues[c] * Math.log(sig_y_ic) - (1 - classValues[c]) * Math.log(1 - sig_y_ic);
        
        return returnVal;
    }

    // compute the accuracy loss of the train set
    public double accuracyLossTrainSet() {
        double accuracyLoss = 0;

        for (int i = 0; i < train.length; i++) {
            preCompute(D_train[i], E_train[i], Psi_train[i], M_train[i], sigY_train[i], train[i]);

            for (int c = 0; c < C; c++) {
                accuracyLoss += accuracyLoss(M_train[i], classValues_train[i], c);
            }
        }
       
        return accuracyLoss/train.length;
    }
    
    public void learnF(int c, int i)
    {
        preCompute(D_train[i], E_train[i], Psi_train[i], M_train[i], sigY_train[i], train[i]);

        dLdY = -(classValues_train[i][c] - sigY_train[i][c]);

        for (int r = 0; r < R; r++) {

            for (int k = 0; k < shapelets[r].length; k++) {

                // update the weights
                gradW_crk=dLdY*M_train[i][r][k] + regWConst*W[c][r][k];
                gradHistW[c][r][k] += gradW_crk*gradW_crk;
                W[c][r][k] -= (eta / ( Math.sqrt(gradHistW[c][r][k]) + eps))*gradW_crk; 

                // update the shapelets
                
                tmp1 = (2.0 / ((double) L[r] * Psi_train[i][r][k]));

                // precompute the term for speed up
                for (int j = 0; j < numberOfSegments[r]; j++)
                    tmp2[r][j] = E_train[i][r][k][j] * (1 + alpha * (D_train[i][r][k][j] - M_train[i][r][k]));

                for (int l = 0; l < L[r]; l++) {

                    tmp3 = 0;
                    for (int j = 0; j < numberOfSegments[r]; j++) 
                        tmp3 += tmp2[r][j] * (shapelets[r][k][l] - train[i][j + l]);

                    gradS_rkl =  dLdY * W[c][r][k] * tmp1 * tmp3;
                    gradHistShapelets[r][k][l] += gradS_rkl*gradS_rkl;
                    shapelets[r][k][l] -= (eta / ( Math.sqrt(gradHistShapelets[r][k][l]) + eps))* gradS_rkl;
                }
            }
        }

        gradBiasW_c = dLdY;
        gradHistBiasW[c] += gradBiasW_c*gradBiasW_c;
        biasW[c] -= (eta / ( Math.sqrt(gradHistBiasW[c]) + eps))*gradBiasW_c; 
    }
            
    public void learnF() {       

        for (int c = 0; c < C; c++)
            for (int i=0; i<train.length; i++) 
            {
                // get a random index from the positive instances of this class
                int posIdx = posIdxs.get(c).get( rand.nextInt(posIdxs.get(c).size()) );
                // get a random index from the negative instances of this class
                int negIdx = negIdxs.get(c).get( rand.nextInt(negIdxs.get(c).size()) );
                
                // learn the model parameters acording to the objective
                // of a random positive and negative class
                learnF(c, posIdx);
                learnF(c, negIdx); 
            } 
    }
    
   
    // build a classifier using cross-validation to tune hyper-parameters
    public void buildClassifier(Instances trainData) throws Exception {
        long startTime=System.nanoTime();
        
        if(paraSearch){
            double[] paramsLambdaW;
            double[] paramsPercentageOfSeriesLength;
            int[] paramsShapeletLengthScale;

            paramsLambdaW=lambdaWRange;
            paramsPercentageOfSeriesLength=percentageOfSeriesLengthRange;
            paramsShapeletLengthScale=shapeletLengthScaleRange;

            int noFolds = 2; 
            double bsfAccuracy = 0;
            int[] params = {0,0,0};
            double accuracy = 0;

            // randomize and stratify the data prior to cross validation
            trainData.randomize(rand); 
            trainData.stratify(noFolds); 

            int numHpsCombinations=1;


            for (int i = 0; i < paramsLambdaW.length; i++) {
                for (int j = 0; j < paramsPercentageOfSeriesLength.length; j++) {
                    for (int k = 0; k < paramsShapeletLengthScale.length; k++) {

                        percentageOfSeriesLength = paramsPercentageOfSeriesLength[j];
                        R = paramsShapeletLengthScale[k];
                        lambdaW = paramsLambdaW[i];

                        print("HPS Combination #"+numHpsCombinations+": {R="+R + 
                                ", L="+percentageOfSeriesLength + ", lambdaW="+lambdaW + "}" ); 
                        print("--------------------------------------");

                        double sumAccuracy = 0;
                        //build our test and train sets. for cross-validation.
                        for (int l = 0; l < noFolds; l++) {
                            Instances trainCV = trainData.trainCV(noFolds, l);
                            Instances testCV = trainData.testCV(noFolds, l);

                            // fixed hyper-parameters
                            eta = 0.1;
                            alpha = -30;
                            maxIter=300;

                            print("Learn model for Fold-"+l + ":" ); 

                            train(trainCV);

                            //test on the remaining fold.
                            accuracy = utilities.ClassifierTools.accuracy(testCV, this);
                            sumAccuracy += accuracy;

                            print("Accuracy-Fold-"+l + " = " + accuracy ); 

                            trainCV=null;
                            testCV=null;
                        }
                        sumAccuracy/=noFolds;

                        print("Accuracy-CV = " + sumAccuracy ); 
                        print("--------------------------------------"); 

                        if(sumAccuracy > bsfAccuracy){
                            int[] p = {i,j,k};
                            params = p;
                            bsfAccuracy = sumAccuracy;
                        }

                        numHpsCombinations++; 
                    }
                }
            }

            System.gc();
            maxAcc=bsfAccuracy;
            lambdaW = paramsLambdaW[params[0]];
            percentageOfSeriesLength = paramsPercentageOfSeriesLength[params[1]];
            R = paramsShapeletLengthScale[params[2]];

            eta = 0.1; 
            alpha = -30;
            maxIter=600;
            print("Learn final model with best hyper-parameters: R="+R
                                +", L="+percentageOfSeriesLength + ", lambdaW="+lambdaW); 
        }
        else{
            fixParameters();
            print("Fixed parameters: R="+R
                                +", L="+percentageOfSeriesLength + ", lambdaW="+lambdaW); 
        }
        
        train(trainData);

        trainResults.setTimeUnit(TimeUnit.NANOSECONDS);
        trainResults.setBuildTime(System.nanoTime()-startTime);
        
    }
    
    private void train(Instances data) throws Exception
    {

        trainSet = data;
        seriesLength = trainSet.numAttributes() - 1; //so we don't include the classLabel at the end.

        nominalLabels = readNominalTargets(trainSet);
        
        if(nominalLabels.size() < 2)
        {
            System.err.println("Fatal error: Number of classes is " + nominalLabels.size());
            return;
        }
        
        //convert the training set into a 2D Matrix.
        train = fromWekaInstancesArray(trainSet, true);

        // Z-normalize the training time seriee
//        for(int i=0; i<train.length; i++)
//            train[i] = StatisticalUtilities.normalize(train[i]);
       
        // initialize the data structures
        initialize();

        // apply the stochastic gradient descent in a series of iterations
        for (int iter = 0; iter <= maxIter; iter++) {
            // learn the latent matrices
           learnF();
            
            // measure the loss
            if ((iter %(maxIter/3)) == 0 && iter>0) 
            {
                double lossTrain = accuracyLossTrainSet();

                print("Iter="+iter+", Loss="+lossTrain); 
                
                // if divergence is detected break
                if ( Double.isNaN(lossTrain) )
                    break;
            }
        }
    }

    @Override
    public double classifyInstance(Instance instance) throws Exception {
            
        
        double[] temp = instance.toDoubleArray();
//remove the class value
        double[] test=new double[temp.length-1];
        System.arraycopy(temp, 0, test, 0, temp.length-1);
        
        // z-normalize time series
        test = StatisticalUtilities.normalize(test);
        
        // initialize the terms for pre-computation
        D_test = new double[R][K][];
        E_test = new double[R][K][];

        for (int r = 0; r < R; r++) {
            for (int k = 0; k < K; k++) {
                D_test[r][k] = new double[numberOfSegments[r]];
                E_test[r][k] = new double[numberOfSegments[r]];
            }
        }

        // initialize the placeholders for the precomputed values
        M_test = new double[R][K];
        Psi_test = new double[R][K];
        sigY_test = new double[C];
        
        preCompute(D_test, E_test, Psi_test, M_test, sigY_test, test);


        double max_Y_hat_ic = Double.MIN_VALUE;
        int label_i = 0;

        for (int c = 0; c < C; c++) {
            double Y_hat_ic = calculateSigmoid(predict_i(M_test, c));

            if (Y_hat_ic > max_Y_hat_ic) {
                max_Y_hat_ic = Y_hat_ic;
                label_i = c;
            }
        }

        return nominalLabels.get(label_i);
    }
    
    
    void print(String s){
        if(debug)
            System.out.println(s);
    }


    @Override
    public Capabilities getCapabilities() {
        throw new UnsupportedOperationException("Not supported yet."); //To change body of generated methods, choose Tools | Templates.
    }
    
    public static ArrayList<Double> readNominalTargets(Instances instances)
    {
        if (instances.size() <= 0)  return null;
        
        ArrayList<Double> nominalLabels = new ArrayList<>();
        
        for (Instance ins : instances) {
            boolean alreadyAdded = false;

            for (Double nominalLabel : nominalLabels) {
                if (nominalLabel == ins.classValue()) {
                    alreadyAdded = true;
                    break;
                }
            }

            if (!alreadyAdded) {
                nominalLabels.add(ins.classValue());
            }
        }

        Collections.sort(nominalLabels);

        return nominalLabels;
    }
    
    
    public static void main(String[] args) throws Exception{
        
        if( args.length == 0 )
            	args = new String[]{"C:\\LocalData\\Dropbox\\TSC Problems", "OliveOil"}; 
        
        //resample 1 of the italypowerdemand dataset
        String dataset = args[1];
        String fileExtension = File.separator + dataset + File.separator + dataset;
        String samplePath = args[0] + fileExtension;

        //load the train and test.
        Instances testSet = DatasetLoading.loadDataNullable(samplePath + "_TEST");
        Instances trainSet = DatasetLoading.loadDataNullable(samplePath + "_TRAIN");        

        LearnShapelets ls = new LearnShapelets();
        ls.setSeed(0);
        ls.buildClassifier(trainSet);
        
        double accuracy = utilities.ClassifierTools.accuracy(testSet, ls);
        System.out.println(dataset+", LS= " + (1 - accuracy));
    }
}
